Diagnostic and Predictive Accuracy of Blood Pressure Screening Methods With Consideration of Rescreening Intervals: A Systematic Review for the U.S. Preventive Services Task ForceFREE
Elevated blood pressure (BP) is the largest contributing risk factor to all-cause and cardiovascular mortality.
To update a systematic review on the benefits and harms of screening for high BP in adults and to summarize evidence on rescreening intervals and diagnostic and predictive accuracy of different BP methods for cardiovascular events.
Selected databases searched through 24 February 2014.
Fair- and good-quality trials and diagnostic accuracy and cohort studies conducted in adults and published in English.
One investigator abstracted data, and a second checked for accuracy. Study quality was dual-reviewed.
Ambulatory BP monitoring (ABPM) predicted long-term cardiovascular outcomes independently of office BP (hazard ratio range, 1.28 to 1.40, in 11 studies). Across 27 studies, 35% to 95% of persons with an elevated BP at screening remained hypertensive after nonoffice confirmatory testing. Cardiovascular outcomes in persons who were normotensive after confirmatory testing (isolated clinic hypertension) were similar to outcomes in those who were normotensive at screening. In 40 studies, hypertension incidence after rescreening varied considerably at each yearly interval up to 6 years. Intrastudy comparisons showed at least 2-fold higher incidence in older adults, those with high-normal BP, overweight and obese persons, and African Americans.
Few diagnostic accuracy studies of office BP methods and protocols in untreated adults.
Evidence supports ABPM as the reference standard for confirming elevated office BP screening results to avoid misdiagnosis and overtreatment of persons with isolated clinic hypertension. Persons with BP in the high-normal range, older persons, those with an above-normal body mass index, and African Americans are at higher risk for hypertension on rescreening within 6 years than are persons without these risk factors.
Primary Funding Source:
Agency for Healthcare Research and Quality.
Nearly 1 in 3 U.S. adults has high blood pressure (BP), including two thirds of those aged 60 years or older (1). Elevated BP is the largest contributing risk factor to all-cause and cardiovascular mortality (2). Despite the clear importance of accurate diagnosis of high BP, recommendations for BP measurement protocols and rescreening intervals are not based on systematic reviews of the literature (3, 4), and recommended protocols, such as repeated measurements, are rarely followed in routine health care settings (5–9). To help address these issues, newer measurement methods have been developed to reduce error, simplify performance of repeated measurements, evaluate BP throughout the 24-hour cycle, and allow use in nonmedical settings. Evidence-based measurement methods and rescreening intervals could improve the benefits and efficiency of BP screening.
In 2007, the U.S. Preventive Services Task Force (USPSTF) reaffirmed its 2003 A recommendation to screen for high BP in adults aged 18 years or older (10). In 2003, a synthesis of indirect evidence for BP screening found good-quality evidence that treatment of high BP in adults substantially decreases the incidence of cardiovascular events (11). Both reviews found that screening and treatment for high BP cause few major harms (11, 12). Given the strong evidence base for the previous recommendations and recently updated guidelines for BP control (4, 13), the USPSTF did not believe that updating the indirect evidence path was necessary. However, the previous systematic reviews did not identify a BP measurement reference standard, address diagnostic accuracy of BP measurement methods and protocols, or determine the most appropriate rescreening interval. Our evidence review was designed to address these important aspects of screening for high BP and update the direct evidence of benefits and harms of screening.
To conduct this review, we developed an analytic framework with 5 key questions (Appendix Figure 1) that examined direct evidence for the benefits and harms of screening for high BP (key questions 1 and 5, respectively), diagnostic accuracy of office BP measurement (OBPM) (key question 2), prediction of cardiovascular events by BP method and diagnostic accuracy of nonoffice measurement (key question 3), and rescreening interval (key question 4). Detailed methods are available in our full evidence report (14). The analytic framework, review questions, and methods for locating and qualifying evidence were posted on the USPSTF Web site for public comment before we started the review, and the final versions reflect public input.
Data Sources and Searches
We searched MEDLINE, PubMed, the Cochrane Central Register of Controlled Trials, and CINAHL from 2003 through 8 August 2014 to update benefits and harms of screening for high BP. We searched the same databases (excluding CINAHL) through 24 February 2014 as follows: starting in 1992 (to allow for implementation of the first guidelines for validation of BP monitoring devices ) for prediction of cardiovascular events by BP method and diagnostic accuracy of nonoffice measurement, and starting in 1966 (the beginning of MEDLINE) for rescreening interval. On the basis of the findings from these updated searches, we did not further update them because any studies we found would probably not have changed the overall conclusions. We also searched bibliographies of relevant reviews, included studies, and publication lists of highly referenced studies.
Two investigators independently reviewed abstracts and full-text articles against prespecified inclusion and exclusion criteria (14). We required all studies to have enrolled untreated adults and to have been conducted in countries rated as “very high” on the 2013 Human Development Index (16). For prediction of cardiovascular events, we allowed studies that included treated patients because a proportion of persons followed over time would inevitably begin treatment. Ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM) devices were eligible for use in confirming an initially elevated OBPM result. For screening benefits and harms, cardiovascular events we analyzed included fatal or nonfatal myocardial infarction; sudden cardiac death; stroke; heart failure; atrial fibrillation; transient ischemic attack; end-stage kidney disease; or a composite of any of the aforementioned events, excluding cardiovascular symptoms, angina, revascularization, carotid intima–media thickness, and left ventricular hypertrophy.
For diagnostic accuracy of OBPM, we included studies that compared different office-based devices or measurement protocols and reported sensitivity, specificity, predictive values, or concordance (for example, κ). For diagnostic accuracy of confirmatory BP measurement methods, eligible study populations had an initial elevated office BP at screening, which allowed for reporting or calculation of the positive predictive value (PPV).
For prediction of cardiovascular events, eligible studies followed a cohort of patients over time and reported the associations (hazard or risk ratios) of BP as a continuous variable, measured by at least 2 methods at baseline, with data on overall mortality or cardiovascular events collected during follow-up. For rescreening interval, we included studies that followed cohorts of initially nonhypertensive adults over time and reported hypertension incidence at rescreening intervals of up to 6 years.
Data Extraction and Quality Assessment
One investigator abstracted data from all included studies, and a second checked for accuracy. Two investigators independently assessed the quality of included studies by using predefined, design-specific criteria (17–19). We rated study quality as good, fair, or poor and excluded all poor-quality studies (17). We resolved disagreements about quality through discussion with a third investigator. Where reported, studies with various threats to internal validity were downgraded to fair-quality according to USPSTF standards (17).
Data Synthesis and Analysis
We qualitatively described the results on the benefits and harms of screening. Per our protocol, we first calculated the diagnostic accuracy of OBPM by using the recommendations of the American Heart Association as the reference standard because there is no gold standard for BP measurement (3). With the subsequent identification of ABPM as the best predictor of cardiovascular events, we calculated the diagnostic accuracy of OBPM and confirmatory BP measurement methods by using ABPM as the reference standard where possible. We qualitatively described all diagnostic accuracy results because data were insufficient for quantitative synthesis.
For prediction of cardiovascular events, we combined fatal and nonfatal events within outcome categories (cardiovascular, stroke, and cardiac). Risk was most commonly reported as the hazard ratio associated with each 10–mm Hg increase in systolic BP and each 5–mm Hg increase in diastolic BP. We converted hazard ratios to these common increments if they were reported differently (14). We depicted the hazard ratios in forest plots for qualitative evaluation; because of the small numbers of studies for each outcome and heterogeneity across studies, we did not calculate summary meta-analytic estimates of risk to determine the best BP measurement method for prediction. We conducted exploratory meta-analyses to compare ABPM protocols (24-hour, daytime, and nighttime) by generating estimates of cardiovascular events or mortality risk for each protocol by using the DerSimonian–Laird random-effects method (20). In sensitivity analyses, these results were compared to estimates generated by using profile likelihood (21) and Knapp–Hartung methods (22).
For rescreening, we pooled reported incidence rates across all studies to generate a weighted mean incidence at yearly intervals (reported within ± 0.5 year). We qualitatively examined within-study comparisons among a priori subgroups of age, BP, sex, body mass index (BMI), smoking status, and race/ethnicity (14).
When constructing the overall summary of evidence (Appendix Table 1), we evaluated included studies within the context of each review question for consistency of results for important outcomes and relevance to primary care.
Role of the Funding Source
Staff from the Agency for Healthcare Research and Quality (AHRQ) provided oversight for the project and assisted in external review of the companion draft evidence synthesis. Liaisons for the USPSTF helped to resolve issues about the scope of the review but were not involved in the conduct of the review.
We reviewed 19 309 abstracts and 1171 articles for possible inclusion (Appendix Figure 2).
Benefits of Screening for High BP
For direct evidence of screening benefit, we included only randomized, controlled trials (RCTs) that reported changes in health outcomes as a result of screening for hypertension compared with no screening. We identified 1 good-quality cluster RCT of a community pharmacy–based BP screening program targeting adults aged 65 years or older (23). Trained volunteer health educators also provided participants with educational materials and resources to support self-management. This trial found fewer annual composite cardiovascular-related hospitalizations in the intervention group than in the control group (rate ratio, 0.91 [95% CI, 0.86 to 0.97]; P = 0.002). When the data were analyzed by the number of unique patients hospitalized, only the reduction in admissions for acute myocardial infarction was statistically significant (rate ratio, 0.89 [CI, 0.79 to 0.99]; P = 0.03). End-stage kidney disease outcomes were not reported. Summaries of the limitations, consistency, and applicability of the evidence for all key questions can be found in Appendix Table 1.
Diagnostic Accuracy of OBPM
We identified 4 good-quality (24–27) and 3 fair-quality (28–30) studies examining the diagnostic accuracy of OBPM methods or protocols in untreated screening populations. Four of these studies (25–28) examined how well automated oscillometric OBPM (1 to 3 measurements) predicted results from manual sphygmomanometry (the reference standard). Among these, 3 studies (26–28) reported sensitivities of oscillometric OBPM ranging from 51% to 68% for elevated BP (systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg), as measured by the reference method. The fourth study (25) reported a sensitivity of 91% but differed from the others in that it used a higher threshold in its definition of elevated BP (systolic BP ≥160 mm Hg or diastolic BP ≥95 mm Hg) and used a research design that minimized human error in manual BP measurement. The fair-quality study (28) reported the lowest sensitivity and used 3 different oscillometric devices, with no attempt to ensure comparability or validity among them. Overall, these 4 studies reported more consistent specificities (97% to 98%) and PPVs (76% to 84%). In 3 studies (31–33) that compared manual and automated OBPM with ABPM as the reference standard, neither manual nor automated systolic OBPM results were clearly favored.
Three diagnostic accuracy studies examined the effect of different aspects of recommended protocols for OBPM (24, 29, 30) in untreated screening populations. For investigating the value of repeated measurements, a single manual BP measurement had a high sensitivity (95%) but a moderate PPV (76%) for the average of the second and third measurements in 1 study with a protocol that included a 5-minute premeasurement rest (24). One small study found elevated BP within the normal range among normotensive participants whose legs were crossed during measurement (29), and another found falsely elevated BP above the hypertensive threshold 40 minutes after caffeine ingestion among 17% of normotensive participants (30).
Prediction of Cardiovascular Events by BP Measurement Method
We identified a reference standard for BP measurement by comparing the accuracy of ambulatory and home-based confirmatory measurement methods with office-based methods for predicting overall mortality and cardiovascular outcomes.
We evaluated the predictive value of ABPM methods for long-term cardiovascular events, after adjustment for OBPM, in 6 good-quality (34–39) and 5 fair-quality (40–44) studies. The ABPM devices used in the included trials are generally still available in the United States and have been validated against at least 1 recognized protocol (www.dableducational.org). Where reported, all ABPM devices were oscillometric and typically took measurements every 15 to 30 minutes during the day and every 30 to 60 minutes at night (Appendix Table 2). Outcomes for 24-hour, daytime, and nighttime monitoring cycles were reported in 8, 10, and 9 studies, respectively. One study that monitored BP for 48 hours was grouped with those monitoring for 24 hours (36). Results did not seem to vary by geographic region or population baseline characteristics. Each 10–mm Hg increment in 24-hour systolic ABPM, adjusted for OBPM, was consistently and statistically significantly associated with an increased risk for fatal and nonfatal stroke in 4 studies (38, 39, 41, 44). Hazard ratios ranged from 1.28 to 1.40 (Figure 1). In 6 studies, each 10–mm Hg increment in 24-hour systolic ABPM, adjusted for OBPM, was associated with an increased risk for fatal and nonfatal cardiovascular events. These results were statistically significant in 5 studies (Figure 1) (34, 36, 38, 41, 43). Hazard ratios ranged from 1.11 to 1.42. One additional study (42) reported only that ABPM predicted cardiovascular mortality in a model that included OBPM (P < 0.001). Estimates of hazard ratios for each 5–mm Hg increment in diastolic 24-hour ABPM, adjusted for OBPM, were also generally statistically significant but were more attenuated (data not shown) (14).
We conducted an unplanned, exploratory meta-analysis to look for relative differences among ABPM protocols. This analysis showed no apparent differences in hazard ratios for each 10–mm Hg increase in systolic BP (24-hour ABPM hazard ratio, 1.24 [CI, 1.17 to 1.30; I2 = 8.7%]; daytime ABPM hazard ratio, 1.20 [CI, 1.12 to 1.28; I2 = 33.3%]; nighttime ABPM hazard ratio, 1.24 [CI, 1.17 to 1.31; I2 = 25.6%] [all controlled for OBPM]). A sensitivity analysis that used 2 additional meta-analytic methods also did not show any differences among protocols.
We also evaluated the predictive value of HBPM for long-term cardiovascular events in 5 good-quality studies (35, 45–48), 4 of which adjusted for OBPM. All showed statistically significant associations with an increased risk for cardiovascular and mortality outcomes, with hazard ratios ranging from 1.17 to 1.39 (Appendix Figure 3).
Diagnostic Accuracy of Methods to Confirm Elevated Office BP
We considered confirmatory BP measurement methods separately from screening OBPM to identify persons who have an elevated BP at screening but are normotensive after confirmatory testing in a nonmedical setting (isolated clinic hypertension). Without confirmatory follow-up, this group may be harmed by misdiagnosis and unnecessary treatment.
We evaluated the diagnostic accuracy of confirmatory BP measurement methods by using ABPM as the reference standard, where available, subsequent to an elevated BP at screening in 6 good-quality (32, 49–53) and 21 fair-quality (31, 33, 40, 54–71) studies. Across 24 comparable studies allowing calculation, the proportion of persons with an elevated BP at screening who were hypertensive on confirmatory testing by ABPM or HBPM ranged from 35% to 95% (Figure 2). Four studies also confirmed hypertension in 58% to 96% of persons who repeated BP measurement at subsequent office visits with the same methods used at the initial screening (data not shown). Study population characteristics related to increased hypertension prevalence, such as older average age, a higher number of abnormal screening results before confirmatory testing, and a higher BP at screening, seemed to be qualitatively associated with a higher PPV for ABPM-confirmed hypertension. On the basis of screening measurement alone, the likelihood of misdiagnosis of hypertension is greater as measurements approach the threshold for a diagnosis of hypertension.
We investigated whether using different screening and confirmatory measurement methods improves diagnostic accuracy. We found 2 studies that enrolled persons with an elevated office BP and followed up with both ABPM and repeated OBPM by the same screening method at a separate visit, but the results did not consistently show improved results with confirmatory testing (data not shown) (54, 61).
Harms of Screening for High BP
We examined several potential harms in addition to misdiagnosis and unnecessary treatment. One good-quality (72) and 3 fair-quality (73–75) trials found no statistically significant differences in psychological distress or quality of life among participants who were labeled as hypertensive or prehypertensive. One fair-quality cohort study conducted among persons who were previously unaware of their hypertension status found increases in overall absenteeism from work, absenteeism due to illness, and number and duration of illness episodes after labeling that were statistically significant at 1 year (76) and 4 years (77) of follow-up. Four fair-quality cohort studies reported sleep disturbances, discomfort, and restrictions in daily activities during the use of an ABPM device (78–81).
Rescreening Interval and Hypertension Incidence in Screened Normotensive Persons
We identified 15 good-quality (82–96) and 25 fair-quality (53, 97–120) studies that reported hypertension incidence after rescreening, and 39 of these reported incidence by a priori subgroups of interest. Studies enrolled between 275 and 115 736 participants at baseline and evaluated screening intervals of up to 6 years. The largest number (16 studies) reported results for a 5-year interval, and only 2 studies provided data for more than 1 rescreening interval (88, 99). Most studies used a diagnostic threshold of at least 140/90 mm Hg and considered the use of antihypertensive medications equivalent to a BP exceeding the diagnostic threshold. Included studies were conducted in Asia (19 studies), the United States (8 studies), Europe (10 studies), the United Kingdom, and Australia. Twenty-one studies were community-based, 12 were employment-based, and 6 were conducted in clinics.
Incidence estimates varied widely at each rescreening interval (2.2% to 4.4% at 1 year and 2.1% to 28.4% at 5 years) (Figure 3). Studies that diagnosed hypertension on the basis of multiple office visits generally showed lower incidence than those that measured BP at 1 visit. In 2 studies that reported hypertension incidence both with and without repeated OBPM at confirmatory visits, about 55% of first-visit incident hypertension cases were not confirmed (53, 97), which suggests that true incident hypertension at various intervals is likely to be at the lower end of these estimates.
The substantial variation in hypertension incidence across studies is related in part to the criteria used to diagnose, and in some studies confirm, incident hypertension. Some variation probably also arises from differences in study populations, which highlights the importance of identifying subpopulations with a higher risk for incident hypertension that may benefit from targeted or more intensive rescreening.
Rescreening Interval in Subpopulations
Appendix Table 3 shows weighted mean hypertension incidence across studies at rescreening intervals of 1 to 5 years, stratified by a priori subpopulations. We focused our detailed evaluation on studies providing direct within-study comparisons.
Four studies reported incidence by age strata (Appendix Table 4) (53, 87, 89, 109). Hypertension incidence was as much as 2- to 4-fold higher in older persons (aged 40 or 45 to 60 or 65 years) than in younger persons (aged 18 to 40 or 45 years). Similarly, hypertension incidence increased with increasing baseline BP (Appendix Table 5) (85, 90, 91, 95, 107). Incidence consistently tripled between optimal (<120/80 mm Hg) and normal (120 to 129/80 to 84 mm Hg) BP categories and approximately doubled between normal and high-normal (130 to 139/85 to 89 mm Hg) categories. For example, persons with optimal BP had a low probability (2% to 9%) of developing hypertension over a 5-year period.
Hypertension incidence was generally higher among men than women, especially in younger populations (Appendix Table 6). Although incidence was also 2-fold higher in overweight persons and 3-fold higher in obese persons compared with normal-weight persons (Table 1) (53, 111), it was not increased in smokers versus nonsmokers or former smokers (data not shown) (14).
Five studies conducted in the United States reported hypertension incidence at rescreening intervals by race/ethnicity (Table 2) (84, 86, 88, 97, 105). In each study, the incidence for African Americans was nearly 2 or more times higher than for white persons at all intervals. Only 1 study directly compared additional racial or ethnic categories; it reported higher incidence rates for African Americans at 5 years (27.5%) than for Asian, white, or Hispanic persons (16.2% to 21.2%) (86).
An earlier review of indirect evidence and the resulting USPSTF recommendation found that treatment of high BP substantially decreases the incidence of cardiovascular events (10, 12). We examined direct evidence of benefits and harms of screening programs to identify adults with high BP and found a single RCT that targeted adults aged 65 years or older. Among those randomly assigned to screening, there was a small but statistically significant reduction in hospitalizations for acute myocardial infarction. Although the results do not apply to all age groups and were potentially confounded by additional management interventions, they provide supportive evidence for the effects of a BP screening program on target cardiovascular disease events.
We then focused most of our review efforts on BP screening methods and rescreening intervals to determine accurate and timely methods for identifying persons with elevated BP who are likely to benefit from treatment. We first examined BP measurement methods used for initial, office-based screening. Surprisingly, few studies provided sufficient data to compare the diagnostic accuracy of manual sphygmomanometry with that of automated methods in screening populations. Similarly, few studies of OBPM protocols were eligible, and those that were provided limited support for repeating BP measurement at a single visit, avoiding caffeine ingestion before measurement, and keeping legs uncrossed during measurement. Studies that seemed to provide support for other recommendations, such as proper arm positioning (121–123), cuff size (124–126), and cuff deflation speed (127) (but not removal of clothing before cuff placement [122, 128, 129]), primarily reported results in terms of mean values rather than diagnostic categories or enrolled hypertensive populations. Although automated OBPM methods offer the advantages of repeated measurements in the absence of medical personnel, future evidence reviews will need to consider the applicability of the larger number of studies conducted in treated, hypertensive persons to these questions.
Blood pressure measured by mercury sphygmomanometry in the office setting is known to be associated with cardiovascular outcomes (130). We compared ABPM and HBPM with manual office methods and found that systolic ABPM consistently and statistically significantly predicted stroke and other cardiovascular outcomes independently of OBPM. In an exploratory, comparative meta-analysis (n = 13 906), we found no apparent difference among 24-hour, daytime, and nighttime ABPM protocols within our included evidence base. Our results were similar to those of a systematic review by the National Institute for Health and Clinical Excellence (131), which concluded that ABPM was superior for predicting clinical outcomes, with no protocol favored in a qualitative review of the data (n > 17 621). However, we did not evaluate certain outcomes (such as angina or revascularization) or populations with comorbid conditions (such as diabetes or kidney disease) and included only studies conducted in countries rated “very high” on the Human Development Index. Two other large meta-analyses (one that included 13 843 hypertensive patients  and one that analyzed 23 856 hypertensive patients and 9641 randomly recruited persons ) reported that nighttime systolic ABPM was a stronger predictor of cardiovascular events than daytime ABPM or OPBM. Evidence gaps suggested by these conflicting meta-analyses include the influence of treatment and age (133) and of composite outcomes and population composition on the predictive values of 24-hour, daytime, and nighttime ABPM. We also found that systolic HBPM predicted cardiovascular outcomes in a pattern similar to that of ABPM; however, too few studies were available to allow us to draw firm conclusions about HBPM.
On the basis of the prognostic evidence, we selected ABPM as the reference standard for BP measurement and for evaluating the diagnostic accuracy of other measurement methods. We regarded daytime, nighttime, or 24-hour ABPM protocols as acceptable. Improved prediction with ABPM also suggested the need for confirmation of OBPM. We found that OBPM variably predicted “true” hypertension, as defined by ABPM. Despite this variability, hypertension at screening with OBPM was not confirmed by non-OBPM methods in a large proportion of persons. Measurement error and regression to the mean may contribute to false-positive screening results with OBPM. However, some persons without confirmation of elevated BP at screening have isolated clinic hypertension. Studies have reported that the long-term outcomes of these persons are more similar to those of normotensive persons than to those of patients with sustained hypertension (134). An unplanned analysis of patients with isolated clinic hypertension in our included studies of cardiovascular prognosis also suggested that cardiovascular disease outcomes are more similar to those of persons who are normotensive at baseline than to those of persons with sustained hypertension (data not shown) (14). Given the high variability of OBPM for predicting hypertension at confirmatory testing and the importance of identifying persons who truly require treatment, confirmatory measurement is needed to avoid misdiagnosis. Ambulatory BP monitoring provides multiple measurements over time in a nonmedical setting, which potentially avoids measurement error, regression to the mean, and misdiagnosis of isolated clinic hypertension and is best correlated with long-term outcomes.
Our evidence review shows that overdiagnosis of hypertension from unconfirmed office-based screening could result in unnecessary treatment in a substantial number of persons. Although our scope did not include reviewing evidence to determine rates of harms due to unnecessary treatment and did not directly address the proportion of persons who would have isolated clinic hypertension, these considerations will be important for future reviews. We found no evidence of other serious harms of BP screening.
Finally, we investigated the best interval for rescreening of BP after a normal screening result. Guidelines make recommendations for rescreening intervals, but none are evidence-based. We found that estimates of incident hypertension at annual intervals up to 6 years were highly variable. Qualitative analysis identified a trend toward lower estimates and less variability in studies that required confirmation (for example, by repeated measurements or visits) of elevated BP at rescreening. These findings further support the importance of confirmatory BP measurement, whether initially or at rescreening. We conclude that the wide variation in incident hypertension was at least partly driven by the different population characteristics reported in the studies. The incidence of hypertension was higher in older persons, African Americans, those with an above-normal BMI, and those with a high-normal BP.
In summary, the available evidence suggests that repeated measurements may improve the diagnostic accuracy of OBPM for screening. Initially elevated BP measured by office-based methods is best confirmed by ABPM to avoid potential overdiagnosis of isolated clinic hypertension and the potential harms of unnecessary treatment. Studies of rescreening intervals of up to 6 years found a variably high incidence of hypertension overall. Hypertension incidence at rescreening was also higher at shorter intervals for persons with BP in the high-normal range, for older persons, for those with an above-normal BMI, and for African Americans compared with those without these risk factors. These results suggest that time and resources might be better directed toward improved measurement accuracy and timely measurement in higher-risk persons rather than measurement of all persons at every office visit.
Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014;129:e28-292. [PMID: 24352519] doi: 10.1161/01.cir.0000441139.02102.80CrossrefMedlineGoogle Scholar
Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, et al. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA. 2012;307:1273-83. [PMID: 22427615] doi: 10.1001/jama.2012.339CrossrefMedlineGoogle Scholar
Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves JW, Hill MN, et al; Council on High Blood Pressure Research Professional and Public Education Subcommittee. American Heart Association. Recommendations for blood pressure measurement in humans: an AHA scientific statement from the Council on High Blood Pressure Research Professional and Public Education Subcommittee. J Clin Hypertens (Greenwich). 2005;7:102-9. [PMID: 15722655] CrossrefMedlineGoogle Scholar
James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA. 2014;311:507-20. [PMID: 24352797] doi: 10.1001/jama.2013.284427CrossrefMedlineGoogle Scholar
Graves JW, Sheps SG. Does evidence-based medicine suggest that physicians should not be measuring blood pressure in the hypertensive patient? [Editorial]. Am J Hypertens. 2004;17:354-60. [PMID: 15062890] CrossrefMedlineGoogle Scholar
Scherwitz LW, Evans LA, Hennrikus DJ, Vallbona C. Procedures and discrepancies of blood pressure measurements in two community health centers. Med Care. 1982;20:727-38. [PMID: 7121092] CrossrefMedlineGoogle Scholar
Burgess SE, MacLaughlin EJ, Smith PA, Salcido A, Benton TJ. Blood pressure rising: differences between current clinical and recommended measurement techniques. J Am Soc Hypertens. 2011;5:484-8. [PMID: 22015319] doi: 10.1016/j.jash.2011.08.007CrossrefMedlineGoogle Scholar
Minor DS, Butler KR Jr, Artman KL, Adair C, Wang W, McNair V, et al. Evaluation of blood pressure measurement and agreement in an academic health sciences center. J Clin Hypertens (Greenwich). 2012;14:222-7. [PMID: 22458743] doi: 10.1111/j.1751-7176.2012.00599.xCrossrefMedlineGoogle Scholar
Kay LE. Accuracy of blood pressure measurement in the family practice center. J Am Board Fam Pract. 1998;11:252-8. [PMID: 9719346] CrossrefMedlineGoogle Scholar
U.S. Preventive Services Task Force. Screening for high blood pressure: U.S. Preventive Services Task Force reaffirmation recommendation statement. Ann Intern Med. 2007;147:783-6. [PMID: 18056662] doi: 10.7326/0003-4819-147-11-200712040-00009LinkGoogle Scholar
Sheridan S, Pignone M, Donahue K. Screening for high blood pressure: a review of the evidence for the U.S. Preventive Services Task Force. Am J Prev Med. 2003;25:151-8. [PMID: 12880884] CrossrefMedlineGoogle Scholar
Wolff T, Miller T. Evidence for the reaffirmation of the U.S. Preventive Services Task Force recommendation on screening for high blood pressure. Ann Intern Med. 2007;147:787-91. [PMID: 18056663] doi: 10.7326/0003-4819-147-11-200712040-00010LinkGoogle Scholar
Go AS, Bauman MA, ColemanKing SM, Fonarow GC, Lawrence W, Williams KA, et al; American Heart Association. An effective approach to high blood pressure control: a science advisory from the American Heart Association, the American College of Cardiology, and the Centers for Disease Control and Prevention. Hypertension. 2014;63:878-85. [PMID: 24243703] doi: 10.1161/HYP.0000000000000003CrossrefMedlineGoogle Scholar
Piper MA, Evans CV, Burda BU, Margolis KL, O'Connor E, Smith N, et al. Screening for high blood pressure in adults: a systematic evidence review for the U.S. Preventive Services Task Force. Rockville, MD: Agency for Healthcare Research and Quality; 2014. Google Scholar
Verberk WJ, Kroon AA, Kessels AG, de Leeuw PW. Home blood pressure measurement: a systematic review. J Am Coll Cardiol. 2005;46:743-51. [PMID: 16139119] CrossrefMedlineGoogle Scholar
United Nations Development Programme. Human Development Report 2013. The Rise of the South: Human Progress in a Diverse World. New York: United Nations Development Programme; 2013. Google Scholar
Harris RP, Helfand M, Woolf SH, Lohr KN, Mulrow CD, Teutsch SM, et al; Methods Work Group. Third US Preventive Services Task Force. Current methods of the US Preventive Services Task Force: a review of the process. Am J Prev Med. 2001;20:21-35. [PMID: 11306229] CrossrefMedlineGoogle Scholar
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al; QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529-36. [PMID: 22007046] doi: 10.7326/0003-4819-155-8-201110180-00009LinkGoogle Scholar
- 19. Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2014. Accessed at www.ohri.ca/programs/clinical_epidemiology/oxford.asp on 21 January 2014. Google Scholar
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177-88. [PMID: 3802833] CrossrefMedlineGoogle Scholar
Hardy RJ, Thompson SG. A likelihood approach to meta-analysis with random effects. Stat Med. 1996;15:619-29. [PMID: 8731004] CrossrefMedlineGoogle Scholar
Hartung J, Knapp G. A refined method for the meta-analysis of controlled clinical trials with binary outcome. Stat Med. 2001;20:3875-89. [PMID: 11782040] CrossrefMedlineGoogle Scholar
Kaczorowski J, Chambers LW, Dolovich L, Paterson JM, Karwalajtys T, Gierman T, et al. Improving cardiovascular health at population level: 39 community cluster randomised trial of Cardiovascular Health Awareness Program (CHAP). BMJ. 2011;342:d442. [PMID: 21300712] doi: 10.1136/bmj.d442CrossrefMedlineGoogle Scholar
Handler J, Zhao Y, Egan BM. Impact of the number of blood pressure measurements on blood pressure classification in US adults: NHANES 1999–2008. J Clin Hypertens (Greenwich). 2012;14:751-9. [PMID: 23126346] doi: 10.1111/jch.12009CrossrefMedlineGoogle Scholar
Kroke A, Fleischhauer W, Mieke S, Klipstein-Grobusch K, Willich SN, Boeing H. Blood pressure measurement in epidemiological studies: a comparative analysis of two methods. Data from the EPIC-Potsdam Study. European Prospective Investigation into Cancer and Nutrition. J Hypertens. 1998;16:739-46. [PMID: 9663913] CrossrefMedlineGoogle Scholar
Lim YH, Choi SY, Oh KW, Kim Y, Cho ES, Choi BY, et al. Comparison between an automated device and a manual mercury sphygmomanometer in an epidemiological survey of hypertension prevalence. Am J Hypertens. 2014;27:537-45. [PMID: 23764377] doi: 10.1093/ajh/hpt100CrossrefMedlineGoogle Scholar
Ostchega Y, Nwankwo T, Sorlie PD, Wolz M, Zipf G. Assessing the validity of the Omron HEM-907XL oscillometric blood pressure measurement device in a National Survey environment. J Clin Hypertens (Greenwich). 2010;12:22-8. [PMID: 20047626] doi: 10.1111/j.1751-7176.2009.00199.xCrossrefMedlineGoogle Scholar
Pavlik VN, Hyman DJ, Toronjo C. Comparison of automated and mercury column blood pressure measurements in health care settings. J Clin Hypertens (Greenwich). 2000;2:81-86. [PMID: 11416630] MedlineGoogle Scholar
Peters GL, Binder SK, Campbell NR. The effect of crossing legs on blood pressure: a randomized single-blind cross-over study. Blood Press Monit. 1999;4:97-101. [PMID: 10450120] CrossrefMedlineGoogle Scholar
Pincomb GA, Lovallo WR, McKey BS, Sung BH, Passey RB, Everson SA, et al. Acute blood pressure elevations with caffeine in men with borderline systemic hypertension. Am J Cardiol. 1996;77:270-4. [PMID: 8607407] CrossrefMedlineGoogle Scholar
Graves JW, Grossardt BR. Discarding the first of three nurse-auscultatory or oscillometric blood pressure measurements does not improve the association of office blood pressure with ABPM. Blood Press Monit. 2010;15:146-51. [PMID: 20407368] doi: 10.1097/MBP.0b013e328337ce76CrossrefMedlineGoogle Scholar
Myers MG. A proposed algorithm for diagnosing hypertension using automated office blood pressure measurement. J Hypertens. 2010;28:703-8. [PMID: 20150823] doi: 10.1097/HJH.0b013e328335d091CrossrefMedlineGoogle Scholar
Zabludowski JR, Rosenfeld JB. Evaluation of clinic blood pressure measurements: assessment by daytime ambulatory blood pressure monitoring. Isr J Med Sci. 1992;28:345-8. [PMID: 1607269] MedlineGoogle Scholar
Clement DL, De Buyzere ML, De Bacquer DA, de Leeuw PW, Duprez DA, Fagard RH, et al; Office versus Ambulatory Pressure Study Investigators. Prognostic value of ambulatory blood-pressure recordings in patients with treated hypertension. N Engl J Med. 2003;348:2407-15. [PMID: 12802026] CrossrefMedlineGoogle Scholar
Fagard RH, Van Den Broeke C, De Cort P. Prognostic significance of blood pressure measured in the office, at home and during ambulatory monitoring in older patients in general practice. J Hum Hypertens. 2005;19:801-7. [PMID: 15959536] CrossrefMedlineGoogle Scholar
Hermida RC, Ayala DE, Mojón A, Fernández JR. Decreasing sleep-time blood pressure determined by ambulatory monitoring reduces cardiovascular risk. J Am Coll Cardiol. 2011;58:1165-73. [PMID: 21884956] doi: 10.1016/j.jacc.2011.04.043CrossrefMedlineGoogle Scholar
Ingelsson E, Björklund-Bodegård K, Lind L, Arnlöv J, Sundström J. Diurnal blood pressure pattern and risk of congestive heart failure. JAMA. 2006;295:2859-66. [PMID: 16804152] CrossrefMedlineGoogle Scholar
Ohkubo T, Kikuya M, Metoki H, Asayama K, Obara T, Hashimoto J, et al. Prognosis of “masked” hypertension and “white-coat” hypertension detected by 24-h ambulatory blood pressure monitoring 10-year follow-up from the Ohasama study. J Am Coll Cardiol. 2005;46:508-15. [PMID: 16053966] CrossrefMedlineGoogle Scholar
Staessen JA, Thijs L, Fagard R, O'Brien ET, Clement D, de Leeuw PW, et al. Predicting cardiovascular risk using conventional vs ambulatory blood pressure in older patients with systolic hypertension. Systolic Hypertension in Europe Trial Investigators. JAMA. 1999;282:539-46. [PMID: 10450715] CrossrefMedlineGoogle Scholar
Celis H, Staessen JA, Thijs L, Buntinx F, De Buyzere M, Den Hond E, et al; Ambulatory Blood Pressure and Treatment of Hypertension Trial Investigators. Cardiovascular risk in white-coat and sustained hypertensive patients. Blood Press. 2002;11:352-6. [PMID: 12523678] CrossrefMedlineGoogle Scholar
Dolan E, Stanton A, Thijs L, Hinedi K, Atkins N, McClory S, et al. Superiority of ambulatory over clinic blood pressure measurement in predicting mortality: the Dublin Outcome Study. Hypertension. 2005;46:156-61. [PMID: 15939805] CrossrefMedlineGoogle Scholar
Hansen TW, Jeppesen J, Rasmussen S, Ibsen H, Torp-Pedersen C. Ambulatory blood pressure and mortality: a population-based study. Hypertension. 2005;45:499-504. [PMID: 15753229] CrossrefMedlineGoogle Scholar
Gasowski J, Li Y, Kuznetsova T, Richart T, Thijs L, Grodzicki T, et al. Is “usual” blood pressure a proxy for 24-h ambulatory blood pressure in predicting cardiovascular outcomes? Am J Hypertens. 2008;21:994-1000. [PMID: 18600212] doi: 10.1038/ajh.2008.231CrossrefMedlineGoogle Scholar
Mesquita-Bastos J, Bertoquini S, Polónia J. Cardiovascular prognostic value of ambulatory blood pressure monitoring in a Portuguese hypertensive population followed up for 8.2 years. Blood Press Monit. 2010;15:240-6. [PMID: 20616705] doi: 10.1097/MBP.0b013e32833c8b08CrossrefMedlineGoogle Scholar
Asayama K, Ohkubo T, Kikuya M, Obara T, Metoki H, Inoue R, et al. Prediction of stroke by home “morning” versus “evening” blood pressure values: the Ohasama study. Hypertension. 2006;48:737-43. [PMID: 16952977] CrossrefMedlineGoogle Scholar
Bobrie G, Chatellier G, Genes N, Clerson P, Vaur L, Vaisse B, et al. Cardiovascular prognosis of “masked hypertension” detected by blood pressure self-measurement in elderly treated hypertensive patients. JAMA. 2004;291:1342-9. [PMID: 15026401] CrossrefMedlineGoogle Scholar
Niiranen TJ, Hänninen MR, Johansson J, Reunanen A, Jula AM. Home-measured blood pressure is a stronger predictor of cardiovascular risk than office blood pressure: the Finn-Home study. Hypertension. 2010;55:1346-51. [PMID: 20385970] doi: 10.1161/HYPERTENSIONAHA.109.149336CrossrefMedlineGoogle Scholar
Ohkubo T, Imai Y, Tsuji I, Nagai K, Kato J, Kikuchi N, et al. Home blood pressure measurement has a stronger predictive power for mortality than does screening blood pressure measurement: a population-based observation in Ohasama, Japan. J Hypertens. 1998;16:971-5. [PMID: 9794737] CrossrefMedlineGoogle Scholar
Cuspidi C, Sala C, Valerio C, Negri F, Mancia G. Nocturnal blood pressure in untreated essential hypertensives. Blood Press. 2011;20:335-41. [PMID: 21651423] doi: 10.3109/08037051.2011.587280CrossrefMedlineGoogle Scholar
Nasothimiou EG, Tzamouranis D, Rarra V, Roussias LG, Stergiou GS. Diagnostic accuracy of home vs. ambulatory blood pressure monitoring in untreated and treated hypertension. Hypertens Res. 2012;35:750-5. [PMID: 22357523] doi: 10.1038/hr.2012.19CrossrefMedlineGoogle Scholar
Ungar A, Pepe G, Monami M, Lambertucci L, Torrini M, Baldasseroni S, et al. Isolated ambulatory hypertension is common in outpatients referred to a hypertension centre. J Hum Hypertens. 2004;18:897-903. [PMID: 15241442] CrossrefMedlineGoogle Scholar
Andreadis EA, Angelopoulos ET, Tsakanikas AP, Agaliotis GD, Kravvariti SD, Mousoulis GP. Automated office versus home measurement of blood pressure in the assessment of morning hypertension. Blood Press Monit. 2012;17:24-34. [PMID: 22218221] doi: 10.1097/MBP.0b013e3283503760CrossrefMedlineGoogle Scholar
Radi S, Lang T, Lauwers-Cancès V, Chatellier G, Fauvel JP, Larabi L, et al; IHPAF Group. One-year hypertension incidence and its predictors in a working population: the IHPAF study. J Hum Hypertens. 2004;18:487-94. [PMID: 14961044] CrossrefMedlineGoogle Scholar
Fogari R, Corradi L, Zoppi A, Lusardi P, Poletti L. Repeated office blood pressure controls reduce the prevalence of white-coat hypertension and detect a group of white-coat normotensive patients. Blood Press Monit. 1996;1:51-54. [PMID: 10226202] MedlineGoogle Scholar
Gerc V, Favrat B, Brunner HR, Burnier M. Is nurse-measured blood pressure a valid substitute for ambulatory blood pressure monitoring? Blood Press Monit. 2000;5:203-9. [PMID: 11035861] CrossrefMedlineGoogle Scholar
Gustavsen PH, Høegholm A, Bang LE, Kristensen KS. White coat hypertension is a cardiovascular risk factor: a 10-year follow-up study. J Hum Hypertens. 2003;17:811-7. [PMID: 14704724] CrossrefMedlineGoogle Scholar
Hond ED, Celis H, Fagard R, Keary L, Leeman M, O'Brien E, et al; THOP Investigators. Self-measured versus ambulatory blood pressure in the diagnosis of hypertension. J Hypertens. 2003;21:717-22. [PMID: 12658017] CrossrefMedlineGoogle Scholar
Hozawa A, Ohkubo T, Kikuya M, Yamaguchi J, Ohmori K, Fujiwara T, et al. Blood pressure control assessed by home, ambulatory and conventional blood pressure measurements in the Japanese general population: the Ohasama study. Hypertens Res. 2002;25:57-63. [PMID: 11924727] CrossrefMedlineGoogle Scholar
Inden Y, Tsuda M, Hayashi H, Takezawa H, Iino S, Kondo T, et al. Relationship between Joint National Committee-VI classification of hypertension and ambulatory blood pressure in patients with hypertension diagnosed by casual blood pressure. Clin Cardiol. 1998;21:801-6. [PMID: 9825191] CrossrefMedlineGoogle Scholar
Kario K. Diagnosis of true uncontrolled hypertension using both home and ambulatory blood pressure monitoring. J Hum Hypertens. 2014;28:176-9. [PMID: 23924872] doi: 10.1038/jhh.2013.73CrossrefMedlineGoogle Scholar
Khoury S, Yarows SA, O'Brien TK, Sowers JR. Ambulatory blood pressure monitoring in a nonacademic setting. Effects of age and sex. Am J Hypertens. 1992;5:616-23. [PMID: 1418850] CrossrefMedlineGoogle Scholar
Licitra R, Acconcia MC, Puddu PE, Pannarale G. Ambulatory blood pressure monitoring in prehypertensive subjects. Cardiovasc Hematol Disord Drug Targets. 2012;12:44-50. [PMID: 22524174] CrossrefMedlineGoogle Scholar
Manning G, Rushton L, Millar-Craig MW. Clinical implications of white coat hypertension: an ambulatory blood pressure monitoring study. J Hum Hypertens. 1999;13:817-22. [PMID: 10618670] CrossrefMedlineGoogle Scholar
Martínez MA, García-Puig J, Martín JC, Guallar-Castillón P, Aguirre de Cárcer A, Torre A, et al; Monitorización Ambulatoria de la Presión Arterial -Area 5 Working Group. Frequency and determinants of white coat hypertension in mild to moderate hypertension MAPA a primary care-based study. Am J Hypertens. 1999;12:251-9. [PMID: 10192226] CrossrefMedlineGoogle Scholar
Pierdomenico SD, Mezzetti A, Lapenna D, Guglielmi MD, Mancini M, Salvatore L, et al. 'White-coat' hypertension in patients with newly diagnosed hypertension: evaluation of prevalence by ambulatory monitoring and impact on cost of health care. Eur Heart J. 1995;16:692-7. [PMID: 7588903] CrossrefMedlineGoogle Scholar
Talleruphuus U, Bang LE, Wiinberg N, Mehlsen J, Svendsen TL, Bentzon MW. Isolated systolic hypertension in an elderly Danish population. Prevalence and daytime ambulatory blood pressure. Blood Press. 2006;15:347-53. [PMID: 17472025] CrossrefMedlineGoogle Scholar
Tanabe P, Persell SD, Adams JG, McCormick JC, Martinovich Z, Baker DW. Increased blood pressure in the emergency department: pain, anxiety, or undiagnosed hypertension? Ann Emerg Med. 2008;51:221-9. [PMID: 18207606] doi: 10.1016/j.annemergmed.2007.10.017CrossrefMedlineGoogle Scholar
Toyama H, Hasegawa Y, Ejima Y, Kurosawa S, Sanada S, Hatano R, et al. Characteristics of young-onset white coat hypertension identified by targeted screening for hypertension at a university health check-up. Hypertens Res. 2008;31:1063-8. [PMID: 18716352] doi: 10.1291/hypres.31.1063CrossrefMedlineGoogle Scholar
Verdecchia P, Schillaci G, Borgioni C, Ciucci A, Zampi I, Gattobigio R, et al. White coat hypertension and white coat effect. Similarities and differences. Am J Hypertens. 1995;8:790-8. [PMID: 7576395] CrossrefMedlineGoogle Scholar
Zawadzka A, Bird R, Casadei B, Conway J. Audit of ambulatory blood pressure monitoring in the diagnosis and management of hypertension in practice. J Hum Hypertens. 1998;12:249-52. [PMID: 9607694] CrossrefMedlineGoogle Scholar
Pessanha P, Viana M, Ferreira P, Bertoquini S, Polónia J. Diagnostic value and cost-benefit analysis of 24 hours ambulatory blood pressure monitoring in primary care in Portugal. BMC Cardiovasc Disord. 2013;13:57. [PMID: 23937261] doi: 10.1186/1471-2261-13-57CrossrefMedlineGoogle Scholar
Spruill TM, Feltheimer SD, Harlapur M, Schwartz JE, Ogedegbe G, Park Y, et al. Are there consequences of labeling patients with prehypertension? An experimental study of effects on blood pressure and quality of life. J Psychosom Res. 2013;74:433-8. [PMID: 23597332] doi: 10.1016/j.jpsychores.2013.01.009CrossrefMedlineGoogle Scholar
Ameling EH, deKorte DF, Man in 't Veld A. Impact of diagnosis and treatment of hypertension on quality of life: a double-blind, randomized, placebo-controlled, cross-over study of betaxolol. J Cardiovasc Pharmacol. 1991;18:752-60. [PMID: 1723773] CrossrefMedlineGoogle Scholar
Mann AH. The psychological effect of a screening programme and clinical trial for hypertension upon the participants. Psychol Med. 1977;7:431-8. [PMID: 905459] CrossrefMedlineGoogle Scholar
Viera AJ, Lingley K, Esserman D. Effects of labeling patients as prehypertensive. J Am Board Fam Med. 2010;23:571-83. [PMID: 20823351] doi: 10.3122/jabfm.2010.05.100047CrossrefMedlineGoogle Scholar
Haynes RB, Sackett DL, Taylor DW, Gibson ES, Johnson AL. Increased absenteeism from work after detection and labeling of hypertensive patients. N Engl J Med. 1978;299:741-4. [PMID: 692548] CrossrefMedlineGoogle Scholar
Taylor DW, Haynes RB, Sackett DL, Gibson ES. Longterm follow-up of absenteeism among working men following the detection and treatment of their hypertension. Clin Invest Med. 1981;4:173-7. [PMID: 7337988] MedlineGoogle Scholar
Verdecchia P, Angeli F, Borgioni C, Gattobigio R, Reboldi G. Ambulatory blood pressure and cardiovascular outcome in relation to perceived sleep deprivation. Hypertension. 2007;49:777-83. [PMID: 17261645] CrossrefMedlineGoogle Scholar
Manning G, Rushton L, Donnelly R, Millar-Craig MW. Variability of diurnal changes in ambulatory blood pressure and nocturnal dipping status in untreated hypertensive and normotensive subjects. Am J Hypertens. 2000;13:1035-8. [PMID: 10981556] CrossrefMedlineGoogle Scholar
Viera AJ, Lingley K, Hinderliter AL. Tolerability of the Oscar 2 ambulatory blood pressure monitor among research participants: a cross-sectional repeated measures study. BMC Med Res Methodol. 2011;11:59. [PMID: 21524301] doi: 10.1186/1471-2288-11-59CrossrefMedlineGoogle Scholar
Nasothimiou EG, Karpettas N, Dafni MG, Stergiou GS. Patients' preference for ambulatory versus home blood pressure monitoring. J Hum Hypertens. 2014;28:224-9. [PMID: 24152822] doi: 10.1038/jhh.2013.104CrossrefMedlineGoogle Scholar
Brantsma AH, Bakker SJ, de Zeeuw D, de Jong PE, Gansevoort RT. Urinary albumin excretion as a predictor of the development of hypertension in the general population. J Am Soc Nephrol. 2006;17:331-5. [PMID: 16434504] CrossrefMedlineGoogle Scholar
Everson SA, Kaplan GA, Goldberg DE, Salonen JT. Hypertension incidence is predicted by high levels of hopelessness in Finnish men. Hypertension. 2000;35:561-7. [PMID: 10679498] CrossrefMedlineGoogle Scholar
Juhaeri, Stevens J, Chambless LE, Tyroler HA, Rosamond W, Nieto FJ, et al. Associations between weight gain and incident hypertension in a bi-ethnic cohort: the Atherosclerosis Risk in Communities Study. Int J Obes Relat Metab Disord. 2002;26:58-64. [PMID: 11791147] CrossrefMedlineGoogle Scholar
Kim J, Kim E, Yi H, Joo S, Shin K, Kim J, et al. Short-term incidence rate of hypertension in Korea middle-aged adults. J Hypertens. 2006;24:2177-82. [PMID: 17053538] CrossrefMedlineGoogle Scholar
Lakoski SG, Cushman M, Siscovick DS, Blumenthal RS, Palmas W, Burke G, et al. The relationship between inflammation, obesity and risk for hypertension in the Multi-Ethnic Study of Atherosclerosis (MESA). J Hum Hypertens. 2011;25:73-9. [PMID: 20944659] doi: 10.1038/jhh.2010.91CrossrefMedlineGoogle Scholar
Lee DH, Ha MH, Kim KY, Jin DG, Jacobs DR Jr. Gamma-glutamyltransferase: an effect modifier in the association between age and hypertension in a 4-year follow-up study. J Hum Hypertens. 2004;18:803-7. [PMID: 15141269] CrossrefMedlineGoogle Scholar
Levine DA, Lewis CE, Williams OD, Safford MM, Liu K, Calhoun DA, et al. Geographic and demographic variability in 20-year hypertension incidence: the CARDIA study. Hypertension. 2011;57:39-47. [PMID: 21135358] doi: 10.1161/HYPERTENSIONAHA.110.160341CrossrefMedlineGoogle Scholar
Morikawa Y, Nakagawa H, Miura K, Ishizaki M, Tabata M, Nishijo M, et al. Relationship between shift work and onset of hypertension in a cohort of manual workers. Scand J Work Environ Health. 1999;25:100-4. [PMID: 10360464] CrossrefMedlineGoogle Scholar
Nakanishi N, Suzuki K, Tatara K. Clustering of cardiovascular risk factors and risk of development of hypertension in Japanese male office workers. J Cardiovasc Risk. 2003;10:213-20. [PMID: 12775955] CrossrefMedlineGoogle Scholar
Vasan RS, Larson MG, Leip EP, Kannel WB, Levy D. Assessment of frequency of progression to hypertension in non-hypertensive participants in the Framingham Heart Study: a cohort study. Lancet. 2001;358:1682-6. [PMID: 11728544] CrossrefMedlineGoogle Scholar
Jung DH, Kim JY, Kim JK, Koh SB, Park JK, Ahn SV. Relative contribution of obesity and serum adiponectin to the development of hypertension. Diabetes Res Clin Pract. 2014;103:51-6. [PMID: 24398319] doi: 10.1016/j.diabres.2013.09.018CrossrefMedlineGoogle Scholar
Völzke H, Fung G, Ittermann T, Yu S, Baumeister SE, Dörr M, et al. A new, accurate predictive model for incident hypertension. J Hypertens. 2013;31:2142-50. [PMID: 24077244] doi: 10.1097/HJH.0b013e328364a16dCrossrefMedlineGoogle Scholar
Yamada Y, Ishizaki M, Kido T, Honda R, Tsuritani I, Ikai E, et al. Alcohol, high blood pressure, and serum gamma-glutamyl transpeptidase level. Hypertension. 1991;18:819-26. [PMID: 1683858] CrossrefMedlineGoogle Scholar
Yambe M, Tomiyama H, Yamada J, Koji Y, Motobe K, Shiina K, et al. Arterial stiffness and progression to hypertension in Japanese male subjects with high normal blood pressure. J Hypertens. 2007;25:87-93. [PMID: 17143178] CrossrefMedlineGoogle Scholar
Klein R, Klein BE, Moss SE, Wong TY. The relationship of retinopathy in persons without diabetes to the 15-year incidence of diabetes and hypertension: Beaver Dam Eye Study. Trans Am Ophthalmol Soc. 2006;104:98-107. [PMID: 17471330] MedlineGoogle Scholar
Apostolides AY, Cutter G, Daugherty SA, Detels R, Kraus J, Wassertheil-Smoller S, et al. Three-year incidence of hypertension in thirteen U.S. communities. Prev Med. 1982;11:487-99. [PMID: 7156059] CrossrefMedlineGoogle Scholar
Arima H, Kiyohara Y, Kato I, Tanizaki Y, Kubo M, Iwamoto H, et al. Alcohol reduces insulin-hypertension relationship in a general population: the Hisayama study. J Clin Epidemiol. 2002;55:863-9. [PMID: 12393073] CrossrefMedlineGoogle Scholar
Bakx JC, Seidell JC, Deurenberg P, van den Hoogen HJ. Development of hypertension in obese subjects seen in general practice. Fam Pract. 1987;4:11-8. [PMID: 3569720] CrossrefMedlineGoogle Scholar
Boyko EJ, Barr EL, Zimmet PZ, Shaw JE. Two-hour glucose predicts the development of hypertension over 5 years: the AusDiab study. J Hum Hypertens. 2008;22:168-76. [PMID: 18046430] CrossrefMedlineGoogle Scholar
Cacciolati C, Hanon O, Dufouil C, Alpérovitch A, Tzourio C. Categories of hypertension in the elderly and their 1-year evolution. The Three-City Study. J Hypertens. 2013;31:680-9. [PMID: 23412428] doi: 10.1097/HJH.0b013e32835ee0caCrossrefMedlineGoogle Scholar
Cheung BM, Ong KL, Tso AW, Leung RY, Xu A, Cherny SS, et al. C-reactive protein as a predictor of hypertension in the Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS) cohort. J Hum Hypertens. 2012;26:108-16. [PMID: 21270838] doi: 10.1038/jhh.2010.125CrossrefMedlineGoogle Scholar
Dernellis J, Panaretou M. Aortic stiffness is an independent predictor of progression to hypertension in nonhypertensive subjects. Hypertension. 2005;45:426-31. [PMID: 15710784] CrossrefMedlineGoogle Scholar
Fagot-Campagna A, Balkau B, Simon D, Ducimetière P, Eschwège E. Is insulin an independent risk factor for hypertension? The Paris Prospective Study. Int J Epidemiol. 1997;26:542-50. [PMID: 9222779] CrossrefMedlineGoogle Scholar
Fitchett G, Powell LH. Daily spiritual experiences, systolic blood pressure, and hypertension among midlife women in SWAN. Ann Behav Med. 2009;37:257-67. [PMID: 19662465] doi: 10.1007/s12160-009-9110-yCrossrefMedlineGoogle Scholar
Giubertoni E, Bertelli L, Bartolacelli Y, Origliani G, Modena MG. Parity as predictor of early hypertension during menopausal transition. J Hypertens. 2013;31:501-7. [PMID: 23196900] doi: 10.1097/HJH.0b013e32835c1742CrossrefMedlineGoogle Scholar
Kim SJ, Lee J, Nam CM, Jee SH, Park IS, Lee KJ, et al. Progression rate from new-onset pre-hypertension to hypertension in Korean adults. Circ J. 2011;75:135-40. [PMID: 21099126] CrossrefMedlineGoogle Scholar
Kivimäki M, Batty GD, Singh-Manoux A, Ferrie JE, Tabak AG, Jokela M, et al. Validating the Framingham Hypertension Risk Score: results from the Whitehall II study. Hypertension. 2009;54:496-501. [PMID: 19597041] doi: 10.1161/HYPERTENSIONAHA.109.132373CrossrefMedlineGoogle Scholar
Lee JH, Yang DH, Park HS, Cho Y, Jun JE, Park WH, et al; HYpertension-Diabetes Daegu Initiative Study Investigators. Incidence of hypertension in Korea: 5-year follow-up study. J Korean Med Sci. 2011;26:1286-92. [PMID: 22022179] doi: 10.3346/jkms.2011.26.10.1286CrossrefMedlineGoogle Scholar
Lee JS, Kawakubo K, Kashihara H, Mori K. Effect of long-term body weight change on the incidence of hypertension in Japanese men and women. Int J Obes Relat Metab Disord. 2004;28:391-5. [PMID: 14724660] CrossrefMedlineGoogle Scholar
Matsuo T, Sairenchi T, Suzuki K, Tanaka K, Muto T. Long-term stable obesity increases risk of hypertension. Int J Obes (Lond). 2011;35:1056-62. [PMID: 21042324] doi: 10.1038/ijo.2010.226CrossrefMedlineGoogle Scholar
Okubo Y, Suwazono Y, Kobayashi E, Nogawa K. An association between smoking habits and blood pressure in normotensive Japanese men: a 5-year follow-up study. Drug Alcohol Depend. 2004;73:167-74. [PMID: 14725956] CrossrefMedlineGoogle Scholar
Satoh H, Saijo Y, Kishi R, Tsutsui H. Brachial-ankle pulse wave velocity is an independent predictor of incident hypertension in Japanese normotensive male subjects. Environ Health Prev Med. 2011;16:217-23. [PMID: 21431793] doi: 10.1007/s12199-010-0189-3CrossrefMedlineGoogle Scholar
Schulz M, Liese AD, Boeing H, Cunningham JE, Moore CG, Kroke A. Associations of short-term weight changes and weight cycling with incidence of essential hypertension in the EPIC-Potsdam Study. J Hum Hypertens. 2005;19:61-7. [PMID: 15343355] CrossrefMedlineGoogle Scholar
Shook RP, Lee DC, Sui X, Prasad V, Hooker SP, Church TS, et al. Cardiorespiratory fitness reduces the risk of incident hypertension associated with a parental history of hypertension. Hypertension. 2012;59:1220-4. [PMID: 22585947] doi: 10.1161/HYPERTENSIONAHA.112.191676CrossrefMedlineGoogle Scholar
Tozawa M, Iseki K, Iseki C, Oshiro S, Higashiuesato Y, Ikemiya Y, et al. Impact of multiple risk factor clustering on the elevation of blood pressure. Hypertens Res. 2002;25:811-6. [PMID: 12484502] CrossrefMedlineGoogle Scholar
Sung KC, Wild SH, Byrne CD. Development of new fatty liver, or resolution of existing fatty liver, over five years of follow-up, and risk of incident hypertension. J Hepatol. 2014;60:1040-5. [PMID: 24445219] doi: 10.1016/j.jhep.2014.01.009CrossrefMedlineGoogle Scholar
Kubo T, Fujino Y, Nakamura T, Kunimoto M, Tabata H, Tsuchiya T, et al. An industry-based cohort study of the association between weight gain and hypertension risk among rotating shift workers. J Occup Environ Med. 2013;55:1041-5. [PMID: 23969502] doi: 10.1097/JOM.0b013e31829731fdCrossrefMedlineGoogle Scholar
Okubo Y, Sairenchi T, Irie F, Yamagishi K, Iso H, Watanabe H, et al. Association of alcohol consumption with incident hypertension among middle-aged and older Japanese population: the Ibarakai Prefectural Health Study (IPHS). Hypertension. 2014;63:41-7. [PMID: 24126168] doi: 10.1161/HYPERTENSIONAHA.113.01585CrossrefMedlineGoogle Scholar
Zambrana RE, López L, Dinwiddie GY, Ray RM, Phillips LS, Trevisan M, et al. Prevalence and incident prehypertension and hypertension in postmenopausal Hispanic women: results from the Women's Health Initiative. Am J Hypertens. 2014;27:372-81. [PMID: 24480867] doi: 10.1093/ajh/hpt279CrossrefMedlineGoogle Scholar
Mourad A, Carney S, Gillies A, Jones B, Nanra R, Trevillian P. Arm position and blood pressure: a risk factor for hypertension? J Hum Hypertens. 2003;17:389-95. [PMID: 12764401] CrossrefMedlineGoogle Scholar
Widener J, Yang C, Costello P, Allen K. Modifications to standard guidelines and changes in blood pressure readings: use of an automatic blood pressure device. AAOHN J. 1999;47:107-13. [PMID: 10347396] CrossrefMedlineGoogle Scholar
Terént A, Breig-Asberg E. Epidemiological perspective of body position and arm level in blood pressure measurement. Blood Press. 1994;3:156-63. [PMID: 8069403] CrossrefMedlineGoogle Scholar
Aylett M, Marples G, Jones K, Rhodes D. Evaluation of normal and large sphygmomanometer cuffs using the Omron 705CP. J Hum Hypertens. 2001;15:131-4. [PMID: 11317193] CrossrefMedlineGoogle Scholar
Guagnano MT, Pace-Palitti V, Murri R, Marchione L, Merlitti D, Sensi S. The prevalence of hypertension in gynaecoid and android obese women. J Hum Hypertens. 1996;10:619-24. [PMID: 8953208] MedlineGoogle Scholar
Bakx C, Oerlemans G, van den Hoogen H, vanWeel C, Thien T. The influence of cuff size on blood pressure measurement. J Hum Hypertens. 1997;11:439-45. [PMID: 9283061] CrossrefMedlineGoogle Scholar
Zheng D, Amoore JN, Mieke S, Murray A. How important is the recommended slow cuff pressure deflation rate for blood pressure measurement? Ann Biomed Eng. 2011;39:2584-91. [PMID: 21735319] doi: 10.1007/s10439-011-0347-9CrossrefMedlineGoogle Scholar
Kahan E, Yaphe J, Knaani-Levinz H, Weingarten MA. Comparison of blood pressure measurements on the bare arm, below a rolled-up sleeve, or over a sleeve. Fam Pract. 2003;20:730-2. [PMID: 14701900] CrossrefMedlineGoogle Scholar
Holleman DR Jr, Westman EC, McCrory DC, Simel DL. The effect of sleeved arms on oscillometric blood pressure measurement. J Gen Intern Med. 1993;8:325-6. [PMID: 8320577] CrossrefMedlineGoogle Scholar
Lewington S, Clarke R, Qizilbash N, Peto R, Collins R; Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360:1903-13. [PMID: 12493255] CrossrefMedlineGoogle Scholar
National Clinical Guideline Centre (UK). Hypertension: The Clinical Management of Primary Hypertension in Adults: Update of Clinical Guidelines 18 and 34. London: National Institute for Health and Clinical Excellence; 2011. [PMID: 22855971] Google Scholar
Roush R, Fagard R, Salles G, Pierdomenico S, Reboldi G, Verdecchia P, et al. Prognostic impact of clinic, daytime, and nighttime systolic blood pressure in 9 cohorts of 13,843 patients with hypertension: systematic review and meta-analysis. J Am Soc Hypertens. 2014;8:e59. CrossrefGoogle Scholar
Hansen TW, Li Y, Boggia J, Thijs L, Richart T, Staessen JA. Predictive role of the nighttime blood pressure. Hypertension. 2011;57:3-10. [PMID: 21079049] doi: 10.1161/HYPERTENSIONAHA.109.133900CrossrefMedlineGoogle Scholar
Pierdomenico SD, Cuccurullo F. Prognostic value of white-coat and masked hypertension diagnosed by ambulatory monitoring in initially untreated subjects: an updated meta analysis. Am J Hypertens. 2011;24:52-8. [PMID: 20847724] doi: 10.1038/ajh.2010.203CrossrefMedlineGoogle Scholar
Author, Article and Disclosure Information
From Kaiser Permanente Center for Health Research, Portland, Oregon, and HealthPartners Institute for Education and Research, Minneapolis, Minnesota.
Note: This review was conducted by the Kaiser Permanente Research Affiliates Evidence-based Practice Center under contract to the AHRQ. AHRQ staff provided oversight for the project and assisted in the external review of the companion draft evidence synthesis. The views expressed in this manuscript do not represent and should not be construed to represent a determination or policy of the AHRQ or the U.S. Department of Health and Human Services.
Acknowledgment: The authors thank the following for their contributions to this project: AHRQ staff; the USPSTF; David B. Callahan, MD, Beverly B. Green, MD, MPH, Joel Handler, MD, James A. Hodgkinson, MD, MSc, Carla I. Mercado, PhD, MS, Martin G. Myers, MD, and George S. Stergiou, MD, for providing expert review of the report; Ning Smith, PhD, for providing statistical expertise; Elizabeth Webber, MS; Leslie A. Perdue, MPH; Keshia Bigler, BS; and Kevin Lutz, MFA, and Smyth Lai, MLS, at the Kaiser Permanente Center for Health Research.
Financial Support: By contract HHSA-290-2012-00151-I, Task Order no. 2 from the AHRQ.
Disclosures: Dr. Piper reports grants from the Agency for Healthcare Research and Quality during the conduct of the study. Ms. Evans reports grants from the Agency for Healthcare Research and Quality during the conduct of the study. Ms. Burda reports grants from the Agency for Healthcare Research and Quality during the conduct of the study. Dr. Margolis reports grants from the Agency for Healthcare Research and Quality during the conduct of the study and grants from the National Heart, Lung, and Blood Institute outside the submitted work. Dr. O'Connor reports grants from the Agency for Healthcare Research and Quality during the conduct of the study. Dr. Whitlock reports grants from the Agency for Healthcare Research and Quality during the conduct of the study. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M14-1539.
Corresponding Author: Reprints are available from the AHRQ Web site (www.ahrq.gov).
Current Author Addresses: Drs. Piper, O'Connor, and Whitlock; Ms. Evans; and Ms. Burda: Kaiser Permanente Center for Health Research, 3800 North Interstate Avenue, Portland, OR 97227.
Dr. Margolis: HealthPartners Institute for Education and Research, Mail Stop 21111R, PO Box 1524, Minneapolis, MN 55440.
Author Contributions: Conception and design: M.A. Piper, C.V. Evans, B.U. Burda, K.L. Margolis, E.P. Whitlock.
Analysis and interpretation of the data: M.A. Piper, C.V. Evans, B.U. Burda, K.L. Margolis, E. O'Connor, E.P. Whitlock.
Drafting of the article: M.A. Piper, C.V. Evans, B.U. Burda.
Critical revision of the article for important intellectual content: M.A. Piper, C.V. Evans, B.U. Burda, K.L. Margolis, E.P. Whitlock.
Final approval of the article: M.A. Piper, C.V. Evans, B.U. Burda, K.L. Margolis, E. O'Connor, E.P. Whitlock.
Statistical expertise: E. O'Connor.
Obtaining of funding: E.P. Whitlock.
Administrative, technical, or logistic support: B.U. Burda.
Collection and assembly of data: M.A. Piper, C.V. Evans, B.U. Burda, K.L. Margolis.
This article was published online first at www.annals.org on 23 December 2014.